Learning Health Systems as Facilitators of Precision Medicine

Using a collaborative learning health system approach to improve disease activity outcomes in children with juvenile idiopathic arthritis in the Pediatric Rheumatology Care and Outcomes Improvement Network

Introduction

The Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN) is a North American learning health network focused on improving outcomes of children with juvenile idiopathic arthritis (JIA). JIA is a chronic autoimmune disease that can lead to morbidity related to persistent joint and ocular inflammation. PR-COIN has a shared patient registry that tracks twenty quality measures including ten outcome measures of which six are related to disease activity. The network's global aim, set in 2021, was to increase the percent of patients with oligoarticular or polyarticular JIA that had an inactive or low disease activity state from 76% to 80% by the end of 2023.

Methods

Twenty-three hospitals participate in PR-COIN, with over 7,200 active patients with JIA. The disease activity outcome measures include active joint count, physician global assessment of disease activity, and measures related to validated composite disease activity scoring systems including inactive or low disease activity by the 10-joint clinical Juvenile Arthritis Disease Activity Score (cJADAS10), inactive or low disease activity by cJADAS10 at 6 months post-diagnosis, mean cJADAS10 score, and the American College of Rheumatology (ACR) provisional criteria for clinical inactive disease. Data is collated to measure network performance, which is displayed on run and control charts. Network-wide interventions have included pre-visit planning, shared decision making, self-management support, population health management, and utilizing a Treat to Target approach to care.

Results

Five outcome measures related to disease activity have demonstrated significant improvement over time. The percent of patients with inactive or low disease activity by cJADAS10 surpassed our goal with current network performance at 81%. Clinical inactive disease by ACR provisional criteria improved from 46% to 60%. The mean cJADAS10 score decreased from 4.3 to 2.6, and the mean active joint count declined from 1.5 to 0.7. Mean physician global assessment of disease activity significantly improved from 1 to 0.6.

Conclusions

PR-COIN has shown significant improvement in disease activity metrics for patients with JIA. The network will continue to work on both site-specific and collaborative efforts to improve outcomes for children with JIA with attention to health equity, severity adjustment, and data quality.

The use and role of digital technology in learning health systems: A scoping review

OBJECTIVE

The review aimed to identify which digital technologies are proposed or used within learning health systems (LHS) and to analyze the extent to which they support learning processes in LHS.

MATERIALS AND METHODS

Multiple databases and grey literature were searched with terms related to LHS. Manual searches and backward searches of reference lists were also undertaken. The review considered publications from 2007 to 2022. Records focusing on LHS, referring to one or more digital technologies, and describing how at least one digital technology could be used in LHS were included.

RESULTS

2046 records were screened for inclusion and 154 records were included in the analysis. Twenty categories of digital technology were identified. The two most common ones across records were data recording and processing and electronic health records. Digital technology was primarily leveraged to support data access and aggregation and data analysis, two of the seven recognized learning processes within LHS learning cycles.

DISCUSSION

The results of the review show that a wide array of digital technologies is being leveraged to support learning cycles within LHS. Nevertheless, an over-reliance on a narrow set of technologies supporting knowledge discovery, a lack of direct evaluation of digital technologies and ambiguity in technology descriptions are hindering the realization of the LHS vision.

CONCLUSION

Future LHS research and initiatives should aim to integrate digital technology to support practice change and impact evaluation. The use of recognized evaluation methods for health information technology and more detailed descriptions of proposed technologies are also recommended.

A New Treatment Program: Focused Integrated Team-based Treatment Program for Bipolar Disorder (FITT-BD)

OBJECTIVE

Bipolar disorder (BD) is complicated by a dynamic, chronic course along with multiple comorbid psychiatric and medical conditions, making it challenging for clinicians to treat and patients to thrive. To efficiently manage the complexity of BD and help patients recover, we developed a Focused Integrated Team-based Treatment Program for Bipolar Disorder (FITT-BD). The purpose of this paper is to describe how we developed this clinic and the lessons we learned.

METHODS

We developed FITT-BD by integrating strategies from stepped care, collaborative care, and learning health care systems. We describe the rationale, details, and lessons learned in developing FITT-BD.

RESULTS

By integrating stepped care, collaborative care, and a learning health care system approach, FITT-BD aims to reduce barriers to care, leverage the expertise of a multidisciplinary treatment team, ensure patient-centeredness, and use assessments to inform and continuously improve outcomes in real time. We learned that there are challenges in the creation of a web-based application that tracks the treatment of patients within a network of hospitals.

CONCLUSIONS

The success of FITT-BD will be determined by the degree to which it can increase treatment access, improve treatment adherence, and help individuals with BD achieve their treatment goals. We expect that FITT-BD will improve outcomes in the context of ongoing clinical care.

PUBLIC HEALTH SIGNIFICANCE

The treatment of BD is challenging and complex. We propose a new treatment model for BD: FITT-BD. We expect that this program will be a patient-centered approach that improves outcomes in the context of ongoing clinical care for patients with BD.

Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature

The "learning health system" (LHS) concept has been defined in broad terms, which makes it challenging for health system leaders to determine exactly what is required to transform their organization into an LHS. This study provides a conceptual map of the LHS landscape by identifying the activities, principles, tools, and conditions that LHS researchers have associated with the concept. Through a multi-step screening process, two researchers identified 79 publications from PubMed (published before January 2020) that contained information relevant to the question, "What work is required of a healthcare organization that is operating as an LHS?" Those publications were coded as to whether or not they referenced each of 94 LHS elements in the taxonomy developed by the study team. This taxonomy, named the Learning Health Systems Consolidated Framework (LHS-CF), organizes the elements into five "bodies of work" (organizational learning, translation of evidence into practice, building knowledge, analyzing clinical data, and engaging stakeholders) and four "enabling conditions" (workforce skilled for LHS work, data systems and informatics technology in place, organization invests resources in LHS work, and supportive organizational culture). We report the frequency that each of the 94 elements was referenced across the 79 publications. The four most referenced elements were: "organization builds knowledge or evidence," "quality improvement practices are standard practice," "patients and family members are actively engaged," and "organizational culture emphasizes and supports learning." By dissecting the LHS construct into its component elements, the LHS-CF taxonomy can serve as a useful tool for LHS researchers and practitioners in defining the aspects of LHS they are addressing. By assessing how often each element is referenced in the literature, the study provides guidance to health system leaders as to how their organization needs to evolve in order to become an LHS - while also recognizing that each organization should emphasize elements that are most aligned with their mission and goals.

Status Toward the Implementation of Precision Dosing in Children

Precision dosing is progressing beyond the conceptual and proof-of-concept stages toward implementation. As the availability of dosing algorithms, tools, and platforms increases, so do the investment in technology services and actual implementation of clinical services offering these solutions to patients. Nowhere is this needed more than in pediatric populations, which are still reliant on adult drug development and bridging strategies to support dosing, often in the absence of actual dose-finding studies in the target pediatric population. Still, there is more work to be done to ensure that proper governance of these services is maintained, and that sustainability of these early implementations is guided by new science as it evolves and meaningful outcome data to confirm that such services deliver on both clinical and economic return on investment. In addition, the field should ensure that all approaches beyond a therapeutic drug monitoring-driven, pharmacokinetic-centric approach should be considered as the tools and services evolve, especially when pediatric-specific pharmacokinetic/pharmacodyamic and pharmacogenetic data are available and shown to be useful to guide dosing. This review evaluates current pediatric precision dosing efforts, highlighting their utility, longevity, and sustainability and assesses the current process for implementing such approaches examining current barriers that stand in the way of broader implementation and the stakeholders that must engage to ensure its ultimate success.

Model-Informed Pediatric Drug Development: Application of Pharmacometrics to Define the Right Dose for Children

One of the biggest challenges in pediatric drug development is defining a safe and effective dose in pediatric populations, which span across a wide age and development range from neonates to adolescents. Model-informed drug development approaches are particularly suited to address knowledge gaps including data leveraging to increase the success of pediatric studies. Considering the often limited number of patients available for study and logistic difficulties to collect the necessary data in pediatric populations, the application of pharmacometrics and modeling and simulation techniques can improve clinical trial efficiency, increase the probability of regulatory success, and optimize therapeutic individualization in support of dedicated trials. This review describes the state of pediatric model-informed drug development to define the right dose for children and provides suggestions for future development.

Measuring engagement in a collaborative learning health system: The case of ImproveCareNow

BACKGROUND

Collaborative learning health systems have demonstrated improved outcomes for a range of different chronic conditions. Patient and healthcare provider engagement in these systems is thought to be associated with improved outcomes. We have adapted an observational framework to measure, and track over time, engagement in ImproveCareNow, a collaborative learning health system for children with inflammatory bowel disease.

INTRODUCTION

We developed a categorical classification scheme for engagement in ImproveCareNow. Each tier is defined in terms of observable individual behaviors. When an individual completes one or more qualifying behavior, s/he is classified as engaged at that tier. Individuals are entered into a database, which is accessible to care centers throughout the ImproveCareNow network. Database records include fields for individual name, behavior type, time, place, and level of engagement.

RESULTS

The resulting system is employed at 79 ImproveCareNow care centers in the United States. The system recognizes four levels of engagement. Behaviors are recorded in a managed vocabulary and recorded in an online database. The database is queried weekly for individual engagement behaviors, which are tracked longitudinally. Center- and network-level statistics are generated and disseminated to stakeholders.

CONCLUSION

It is possible to monitor longitudinal engagement in a collaborative learning health system, thereby charting progress toward engagement goals and enabling quantitative evaluation of interventions aimed at increasing engagement.

Scaling Beyond Early Adopters: a Content Analysis of Literature and Key Informant Perspectives

BACKGROUND

Innovations and improvements in care delivery are often not spread across all settings that would benefit from their uptake. Scale-up and spread efforts are deliberate efforts to increase the impact of innovations successfully tested in pilot projects so as to benefit more people. The final stages of scale-up and spread initiatives must contend with reaching hard-to-engage sites.

OBJECTIVE

To describe the process of scale-up and spread initiatives, with a focus on hard-to-engage sites and strategies to approach them.

DESIGN

Qualitative content analysis of systematically identified literature and key informant interviews.

PARTICIPANTS

Leads from large magnitude scale-up and spread projects.

APPROACH

We conducted a systematic literature search on large magnitude scale-up and spread and interviews with eight project leads, who shared their perspectives on strategies to scale-up and spread clinical and administrative practices across healthcare systems, focusing on hard-to-engage sites. We synthesized these data using content analysis.

KEY RESULTS

Searches identified 1919 titles, of which 52 articles were included. Thirty-four discussed general scale-up and spread strategies, 11 described hard-to-engage sites, and 7 discussed strategies for hard-to-engage sites. These included publications were combined with interview findings to describe a fourth phase of the national scale-up and spread process, common challenges for spreading to hard-to-engage sites, and potential benefits of working with hard-to-engage sites, as well as useful strategies for working with hard-to-engage sites.

CONCLUSIONS

We identified scant published evidence that describes strategies for reaching hard-to-engage sites. The sparse data we identified aligned with key informant accounts. Future work could focus on better documentation of the later stages of spread efforts, including specific tailoring of approaches and strategies used with hard-to-engage sites. Spread efforts should include a "flexible, tailored approach" for this highly variable group, especially as implementation science is looking to expand its impact in routine care settings.

Integrating Genomic Screening into Primary Care: Provider Experiences Caring for Latino Patients at a Community-Based Health Center

INTRODUCTION

Minority communities have had limited access to advances in genomic medicine. Mayo Clinic and Mountain Park Health Center, a Federally Qualified Health Center in Phoenix, Arizona, partnered to assess the feasibility of offering genomic screening to Latino patients receiving care at a community-based health center. We examined primary care provider (PCP) experiences reporting genomic screening results and integrating those results into patient care.

METHODS

We conducted open-ended, semi-structured interviews with PCPs and other members of the health care team charged with supporting patients who received positive genomic screening results. Interviews were recorded, transcribed, and analyzed thematically.

RESULTS

Of the 500 patients who pursued genomic screening, 10 received results indicating a genetic variant that warranted clinical management. PCPs felt genomic screening was valuable to patients and their families, and that genomic research should strive to include underrepresented minorities. Providers identified multiple challenges integrating genomic sequencing into patient care, including difficulties maintaining patient contact over time; arranging follow-up medical care; and managing results in an environment with limited genetics expertise. Providers also reflected on the ethics of offering genomic sequencing to patients who may not be able to pursue diagnostic testing or follow-up care due to financial constraints.

CONCLUSIONS

Our results highlight the potential benefits and challenges of bringing advances in precision medicine to community-based health centers serving under-resourced populations. By proactively considering patient support needs, and identifying financial assistance programs and patient-referral mechanisms to support patients who may need specialized medical care, PCPs and other health care providers can help to ensure that precision medicine lives up to its full potential as a tool for improving patient care.

Some statistical memes which sound correct but aren't quite: Application to the analysis of observational databases used in learning health systems

We consider four memes, correct within the context of randomized trials but requiring modification for the analysis of the observational databases typically associated with learning health systems: (a) the right answer always requires randomization; (b) a bigger database is always a better database; (c) statistical adjustment always works if based on a large enough database; and (d) always make a formal adjustment when testing multiple hypotheses. The rationale for these memes within the context of randomized trials is discussed, and the memes are restated in a fashion that is consistent with learning health systems.

A qualitative study of design stakeholders' views of developing and implementing a registry-based learning health system

BACKGROUND

New opportunities to record, collate, and analyze routine patient data have prompted optimism about the potential of learning health systems. However, real-life examples of such systems remain rare and few have been exposed to study. We aimed to examine the views of design stakeholders on designing and implementing a US-based registry-enabled care and learning system for cystic fibrosis (RCLS-CF).

METHODS

We conducted a two-phase qualitative study with stakeholders involved in designing, implementing, and using the RCLS-CF. First, we conducted semi-structured interviews with 19 program personnels involved in design and delivery of the program. We then undertook 11 follow-up interviews. Analysis of interviews was based on the constant comparative method, supported by NVivo software.

RESULTS

The organizing principle for the RCLS-CF was a shift to more partnership-based relationships between patients and clinicians, founded in values of co-production, and facilitated by technology-enabled data sharing. Participants proposed that, for the system to be successful, the data it collects must be both clinically useful and meaningful to patients and clinicians. They suggested that the prerequisites included a technological infrastructure capable of supporting data entry and joint decision-making in an accessible way, and a set of social conditions, including willingness from patients and clinicians alike to work together in new ways that build on the expertise of both parties. Follow-up interviews highlighted some of the obstacles, including technical challenges and practical constraints on refiguring relationships between clinicians and patients.

CONCLUSIONS

The values and vision underlying the RCLS-CF were shared and clearly and consistently articulated by design stakeholders. The challenges to realization were often not at the level of principle, but were both practical and social in character. Lessons from this study may be useful to other systems looking to harness the power of "big data" registries, including patient-reported data, for care, research, and quality improvement.

Etiology and Medical Management of Pediatric Intestinal Failure

Pediatric intestinal failure occurs when gut function is insufficient to meet the growing child's hydration and nutrition needs. After massive bowel resection, the remnant bowel adapts to lost absorptive and digestive capacity through incompletely defined mechanisms newly targeted for pharmacologic augmentation. Management seeks to achieve enteral autonomy and mitigate the development of comorbid disease. Care has improved, most notably related to reductions in blood stream infection and liver disease. The future likely holds expansion of pharmacologic adaptation augmentation, refinement of intestinal tissue engineering techniques, and the development of a learning health network for efficient multicenter study and care improvement.

Therapeutic drug monitoring to improve outcome of anti-TNF drugs in pediatric inflammatory bowel disease

Introduction: Medical treatment of pediatric inflammatory bowel diseases (IBD) has been greatly changed by the introduction of a number of biologic agents that are able to target various players of the immune response. In particular, monoclonal antibodies against the pro-inflammatory cytokine TNF-alpha (TNF) such as infliximab, adalimumab, and golimumab are now in the clinics both in induction and maintenance therapy, and several efforts are currently ongoing to optimize the use of these drugs in children. Areas covered: This review focuses on therapeutic drug monitoring (TDM) of anti-TNF levels and antidrug antibodies (ADAs), in IBD children. A revision of the analytical assays used for assessing anti-TNF plasma levels is also provided. Expert opinion: Although there is a consensus across studies that higher anti-TNF trough levels are associated with a better clinical outcome, and that early anti-TNF serum measurements could be predictive of long-term response, it is still not clear what the best predictive time of sampling is and what the ideal target drug plasma concentration to achieve. Indeed, there are a number of published studies, particularly in pediatric cohorts, limited by the population size analyzed and more prospective large studies are needed to examine the value of these predictive markers.

Causes of Treatment Failure in Children With Inflammatory Bowel Disease Treated With Infliximab: A Pharmacokinetic Study

OBJECTIVES

Anti-tumor necrosis factor antibodies have led to a revolution in the treatment of inflammatory bowel diseases (IBD); however, a sizable proportion of patients does not respond to therapy. There is increasing evidence suggesting that treatment failure may be classified as mechanistic (pharmacodynamic), pharmacokinetic, or immune-mediated. Data regarding the contribution of these factors in children with IBD treated with infliximab (IFX) are still incomplete. The aim was to assess the causes of treatment failure in a prospective cohort of pediatric patients treated with IFX.

METHODS

This observational study considered 49 pediatric (median age 14.4) IBD patients (34 Crohn disease, 15 ulcerative colitis) treated with IFX. Serum samples were collected at 6, 14, 22 and 54 weeks, before IFX infusions. IFX and anti-infliximab antibodies (AIA) were measured using enzyme linked immunosorbent assays. Disease activity was determined by Pediatric Crohn's Disease Activity Index or Pediatric Ulcerative Colitis Activity Index.

RESULTS

Clinical remission, defined as a clinical score <10, was obtained by 76.3% of patients at week 14 and by 73.9% at week 54. Median trough IFX concentration was higher at all time points in patients achieving sustained clinical remission. IFX levels during maintenance correlated also with C-reactive protein, albumin, and fecal calprotectin. After multivariate analysis, IFX concentration at week 14 >3.11 μg/mL emerged as the strongest predictor of sustained clinical remission. AIA concentrations were correlated inversely with IFX concentrations and directly with adverse reactions.

CONCLUSIONS

Most cases of therapeutic failure were associated with low serum drug levels. IFX trough levels at the end of induction are associated with sustained long-term response.

Male Infertility, Precision Medicine and Systems Proteomics

Precision medicine (PM) is an approach that has the power to create the best effect and safety of medicine and treatment with the least side effects for each person. PM is very helpful as sometimes due to inaccurate or late diagnosis or toxicities of the drugs irreversible side effect for patient's health are generated. This seemingly new and emerging science is also effective in preventing disease, due to differences in the genes, environment, and lifestyles of any particular person. PM can be a prominent criterion in infertility research. To achieve this goal, there should be information from a healthy human body, including genetic and molecular information. A PM is an evolution in health care, which is very helpful even economically. The guarantor of the PM success is the examination of the molecular profile of the patient, including genes, proteins, metabolites, etc. Therefore, genomics, proteomics, and metabolomics-based techniques are very important in this regard. Unfortunately, despite extensive studies on PM practice in various fields, male infertility has remained unresponsive. Given that around 20% of couples around the world suffer from infertility, and almost half of them are related to men's problems, the PM approach has a high potential for male infertility. In this study, with the help of proteomics and metabolomics, PM information on male infertility was explored.

Key Challenges in the Search for Innovative Drug Treatments for Special Populations. Converging Needs in Neonatology, Pediatrics, and Medical Genetics

The explosion of knowledge concerning the interplay of genetic and environmental factors determining pathophysiology and guiding therapeutic choice has altered the landscape in pediatric clinical pharmacology and pharmacy. The need for innovative research methods and design expertise for small clinical trials to be undertaken in sparse populations has been accentuated. At the same time, shortfalls in critical human resources represent a key challenge, especially in low- and middle-income countries where the need for new research and education directions is greatest. Unless a specific action plan is urgently developed, there will be a continuing gap in availability of the essential expertise needed to address treatment challenges in special patient populations such as neonates, patients suffering from rare or neglected diseases, and children of all ages.

Precision Medicine-Nobody Is Average

Medicine gets personal and tailor-made treatments are underway. Hospitals have started to advertise their advanced genomic testing capabilities and even their disruptive technologies to help foster a culture of innovation. The prediction in the lay press is that in decades from now we may look back and see 2017 as the year precision medicine blossomed. It is all part of the Precision Medicine Initiative that takes into account individual differences in people's genes, environments, and lifestyles.